Fingerprint sensing and matching is a reliable and widely used technique for personal identification or verification. In particular, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference fingerprints already in a database to determine proper identification of a person, such as for verification purposes.
A particularly advantageous approach to fingerprint sensing is disclosed in U.S. Pat. No. 5,963,679 to Setlak et al. and assigned to the assignee of the present invention. The fingerprint sensor is an integrated circuit sensor that drives the user's finger with an electric field signal and senses the electric field with an array of electric field sensing pixels on the integrated circuit substrate. Such sensors are used to control access for many different types of electronic devices such as computers, cell phones, personal digital assistants (PDA's), and the like. In particular, fingerprint sensors are used because they may have a small footprint, are relatively easy for a user to use and they provide reasonable authentication capabilities.
Another significant advance in finger sensing technology is disclosed in U.S. Pat. No. 5,953,441 also to Setlak et al., assigned to the assignee of the present invention, and the entire contents of which are incorporated by reference. This patent discloses a fingerprint sensor including an array of impedance sensing elements for generating signals related to an object positioned adjacent thereto, and a spoof reducing circuit for determining whether or not an impedance of the object positioned adjacent the array of impedance sensing elements corresponds to a live finger to thereby reduce spoofing of the fingerprint sensor by an object other than a live finger. A spoofing may be indicated and/or used to block further processing. The spoof reducing circuit may detect a complex impedance having a phase angle in a range of about 10 to 60 degrees corresponding to a live finger. The fingerprint sensor may include a drive circuit for driving the array of impedance sensing elements, and a synchronous demodulator for synchronously demodulating signals from the array of impedance sensing elements.
“Spoof” fingerprints are typically made using natural and artificial materials, such as gelatin, gum, gummy bears, meat products, clay, Play-Doh, auto body filler, resins, metal, etc. that can be used to imitate the ridges and valleys present in a real fingerprint. As it is desirable to be able to acquire a fingerprint image under any skin condition (dry, moist, etc.) some fingerprint sensors employ real-time gain and other adjustments to obtain the best possible images. In doing so, sensors that detect fingerprints using these approaches are sometimes susceptible to attack using spoofs because these systems are capable of imaging widely varying skin conditions (and other materials).
Spoof detection approaches can be broadly classified into hardware and software based approaches. Hardware based approaches typically involve coupling a biometric device to a finger sensor. For example, previous work in the area of spoof detection and reduction may be considered as having used: A.) impedance classification: determining the impedance characteristics of a material over some frequency range; B.) optical dispersion characteristics; C.) thermal measurements; D.) phase setting and signal amplitude; and E.) finger settling detection. In contrast, a software based approach to spoof detection may not involve changes or additions to a finger sensor. A software based approach may involve additional comparisons of finger samples from a user.
Abhyankar et al., Fingerprint Liveness Detection Using Local Ridge Frequencies and Multiresolution Texture Analysis Techniques, Proc. Int. Conf. Image Processing, 2006, discloses adopting statistical features obtained through multi-resolution texture and local-ridge frequency analysis. Classification is performed using a fuzzy c-means classifier. U.S. Pat. No. 7,505,613 to Russo, also assigned to the present assignee, discloses a method of finger spoof detection that is similar to Abhyankar et al., but adds user adaptability.